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Responses to COVID-19 with Probabilistic Programming
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2021-06-01 , DOI: arxiv-2106.00192
Assem Zhunis, Tung-Duong Mai, Sundong Kim

The COVID-19 pandemic left its unique mark on the 21st century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96\% along with a 98\% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.

中文翻译:

使用概率编程对 COVID-19 的响应

COVID-19 大流行在 21 世纪留下了独特的印记,成为历史上最重大的灾难之一,促使世界各国政府采取广泛的干预措施来应对。然而,这些限制伴随着巨大的代价。政府制定反病毒策略以平衡保护公众健康和最小化经济成本之间的权衡至关重要。这项工作提出了一种概率规划方法来量化主要非药物干预的效率。我们提出了一个生成模拟模型,该模型考虑了采用此类策略的经济和人力资本成本,并提供了一个端到端的管道来模拟病毒传播和各种政策组合的损失。通过调查覆盖四大洲 10 个国家的国家反应,我们发现保持社交距离加上接触者追踪是最成功的政策,将病毒传播率降低了 96%,同时经济和人力资本损失降低了 98%。结合实验结果,我们开源了一个框架来测试每种策略组合的功效。
更新日期:2021-06-02
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